Overview

Dataset statistics

Number of variables19
Number of observations289926
Missing cells1531611
Missing cells (%)27.8%
Duplicate rows257
Duplicate rows (%)0.1%
Total size in memory42.0 MiB
Average record size in memory152.0 B

Variable types

Numeric10
Categorical5
Text1
DateTime3

Alerts

Dataset has 257 (0.1%) duplicate rowsDuplicates
ad_description_len is highly overall correlated with ad_typeHigh correlation
ad_type is highly overall correlated with ad_description_len and 1 other fieldsHigh correlation
call_status is highly overall correlated with durationHigh correlation
clicks is highly overall correlated with cost and 2 other fieldsHigh correlation
conversions_calls is highly overall correlated with durationHigh correlation
cost is highly overall correlated with clicks and 1 other fieldsHigh correlation
display_location is highly overall correlated with clicksHigh correlation
duration is highly overall correlated with call_status and 1 other fieldsHigh correlation
headline1_len is highly overall correlated with ad_typeHigh correlation
impressions is highly overall correlated with clicks and 1 other fieldsHigh correlation
currency is highly imbalanced (70.7%)Imbalance
call_type is highly imbalanced (89.0%)Imbalance
call_type has 253026 (87.3%) missing valuesMissing
call_status has 253026 (87.3%) missing valuesMissing
start_time has 253026 (87.3%) missing valuesMissing
duration has 253026 (87.3%) missing valuesMissing
end_time has 253026 (87.3%) missing valuesMissing
display_location has 253026 (87.3%) missing valuesMissing
conversions_calls has 9855 (3.4%) missing valuesMissing
clicks is highly skewed (γ1 = 27.63556565)Skewed
cost is highly skewed (γ1 = 25.78260393)Skewed
conversions is highly skewed (γ1 = 24.77355007)Skewed
clicks has 79042 (27.3%) zerosZeros
cost has 79042 (27.3%) zerosZeros
conversions has 278470 (96.0%) zerosZeros
duration has 9879 (3.4%) zerosZeros
conversions_calls has 258674 (89.2%) zerosZeros
headline1_len has 31252 (10.8%) zerosZeros
headline2_len has 31252 (10.8%) zerosZeros
ad_description_len has 31252 (10.8%) zerosZeros

Reproduction

Analysis started2024-04-06 12:19:19.061690
Analysis finished2024-04-06 12:20:26.849479
Duration1 minute and 7.79 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

impressions
Real number (ℝ)

HIGH CORRELATION 

Distinct1600
Distinct (%)0.6%
Missing400
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean72.80962
Minimum0
Maximum6833
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-04-06T15:20:27.136889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median23
Q369
95-th percentile320
Maximum6833
Range6833
Interquartile range (IQR)63

Descriptive statistics

Standard deviation153.59279
Coefficient of variation (CV)2.1095123
Kurtosis101.96793
Mean72.80962
Median Absolute Deviation (MAD)20
Skewness6.8263544
Sum21080278
Variance23590.746
MonotonicityNot monotonic
2024-04-06T15:20:27.428642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 22642
 
7.8%
2 14500
 
5.0%
3 11327
 
3.9%
4 9673
 
3.3%
5 8523
 
2.9%
6 7554
 
2.6%
7 6847
 
2.4%
8 6346
 
2.2%
9 5802
 
2.0%
10 5346
 
1.8%
Other values (1590) 190966
65.9%
ValueCountFrequency (%)
0 5
 
< 0.1%
1 22642
7.8%
2 14500
5.0%
3 11327
3.9%
4 9673
3.3%
5 8523
 
2.9%
6 7554
 
2.6%
7 6847
 
2.4%
8 6346
 
2.2%
9 5802
 
2.0%
ValueCountFrequency (%)
6833 1
< 0.1%
5607 1
< 0.1%
5407 1
< 0.1%
4991 1
< 0.1%
4938 1
< 0.1%
4704 1
< 0.1%
4608 1
< 0.1%
4488 1
< 0.1%
4403 1
< 0.1%
4169 1
< 0.1%

clicks
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct714
Distinct (%)0.2%
Missing400
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean8.0399273
Minimum0
Maximum4227
Zeros79042
Zeros (%)27.3%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-04-06T15:20:27.709864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile30
Maximum4227
Range4227
Interquartile range (IQR)6

Descriptive statistics

Standard deviation36.241161
Coefficient of variation (CV)4.5076478
Kurtosis1467.1623
Mean8.0399273
Median Absolute Deviation (MAD)2
Skewness27.635566
Sum2327768
Variance1313.4218
MonotonicityNot monotonic
2024-04-06T15:20:28.002975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 79042
27.3%
1 47826
16.5%
2 30843
 
10.6%
3 21876
 
7.5%
4 16519
 
5.7%
5 12557
 
4.3%
6 9842
 
3.4%
7 8119
 
2.8%
8 6655
 
2.3%
9 5654
 
2.0%
Other values (704) 50593
17.5%
ValueCountFrequency (%)
0 79042
27.3%
1 47826
16.5%
2 30843
 
10.6%
3 21876
 
7.5%
4 16519
 
5.7%
5 12557
 
4.3%
6 9842
 
3.4%
7 8119
 
2.8%
8 6655
 
2.3%
9 5654
 
2.0%
ValueCountFrequency (%)
4227 1
< 0.1%
2813 1
< 0.1%
2367 1
< 0.1%
2295 1
< 0.1%
2288 1
< 0.1%
2283 1
< 0.1%
1834 1
< 0.1%
1767 1
< 0.1%
1729 1
< 0.1%
1717 1
< 0.1%

cost
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct82548
Distinct (%)28.5%
Missing400
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2208.5251
Minimum0
Maximum719928
Zeros79042
Zeros (%)27.3%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-04-06T15:20:28.290484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median553
Q32062.5
95-th percentile8684
Maximum719928
Range719928
Interquartile range (IQR)2062.5

Descriptive statistics

Standard deviation7392.6097
Coefficient of variation (CV)3.3473063
Kurtosis1471.5102
Mean2208.5251
Median Absolute Deviation (MAD)553
Skewness25.782604
Sum6.3942542 × 108
Variance54650679
MonotonicityNot monotonic
2024-04-06T15:20:28.577913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 79042
 
27.3%
4 500
 
0.2%
6 333
 
0.1%
23 324
 
0.1%
5 287
 
0.1%
8 254
 
0.1%
10 222
 
0.1%
52 195
 
0.1%
20 182
 
0.1%
18 159
 
0.1%
Other values (82538) 208028
71.8%
(Missing) 400
 
0.1%
ValueCountFrequency (%)
0 79042
27.3%
1 5
 
< 0.1%
1 128
 
< 0.1%
1.249190939 1
 
< 0.1%
1.249706917 1
 
< 0.1%
1.249951647 1
 
< 0.1%
1.249961568 1
 
< 0.1%
2 10
 
< 0.1%
2 130
 
< 0.1%
2.498659517 1
 
< 0.1%
ValueCountFrequency (%)
719928 3
< 0.1%
543780 2
< 0.1%
345962 1
 
< 0.1%
345723 1
 
< 0.1%
337973.75 1
 
< 0.1%
326958.75 1
 
< 0.1%
300990.5296 1
 
< 0.1%
297544 1
 
< 0.1%
296324.6296 1
 
< 0.1%
295796 1
 
< 0.1%

conversions
Real number (ℝ)

SKEWED  ZEROS 

Distinct238
Distinct (%)0.1%
Missing400
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.79013975
Minimum0
Maximum1297
Zeros278470
Zeros (%)96.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-04-06T15:20:28.854171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1297
Range1297
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.298258
Coefficient of variation (CV)13.033464
Kurtosis1296.7188
Mean0.79013975
Median Absolute Deviation (MAD)0
Skewness24.77355
Sum228766
Variance106.05411
MonotonicityNot monotonic
2024-04-06T15:20:29.136537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 278470
96.0%
1 4833
 
1.7%
2 1690
 
0.6%
3 709
 
0.2%
4 422
 
0.1%
5 246
 
0.1%
6 191
 
0.1%
7 160
 
0.1%
8 133
 
< 0.1%
10 81
 
< 0.1%
Other values (228) 2591
 
0.9%
(Missing) 400
 
0.1%
ValueCountFrequency (%)
0 278470
96.0%
1 4833
 
1.7%
2 1690
 
0.6%
3 709
 
0.2%
4 422
 
0.1%
5 246
 
0.1%
6 191
 
0.1%
7 160
 
0.1%
8 133
 
< 0.1%
9 65
 
< 0.1%
ValueCountFrequency (%)
1297 1
 
< 0.1%
426 1
 
< 0.1%
422 6
< 0.1%
421 3
< 0.1%
385 1
 
< 0.1%
382 5
< 0.1%
364 1
 
< 0.1%
362 1
 
< 0.1%
344 4
< 0.1%
334 1
 
< 0.1%

ad_type
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing400
Missing (%)0.1%
Memory size2.2 MiB
EXPANDED_TEXT_AD
148103 
RESPONSIVE_SEARCH_AD
96979 
EXPANDED_DYNAMIC_SEARCH_AD
44444 

Length

Max length26
Median length16
Mean length18.874892
Min length16

Characters and Unicode

Total characters5464772
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEXPANDED_TEXT_AD
2nd rowEXPANDED_TEXT_AD
3rd rowEXPANDED_TEXT_AD
4th rowEXPANDED_TEXT_AD
5th rowEXPANDED_TEXT_AD

Common Values

ValueCountFrequency (%)
EXPANDED_TEXT_AD 148103
51.1%
RESPONSIVE_SEARCH_AD 96979
33.4%
EXPANDED_DYNAMIC_SEARCH_AD 44444
 
15.3%
(Missing) 400
 
0.1%

Length

2024-04-06T15:20:29.404729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T15:20:29.797792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
expanded_text_ad 148103
51.2%
responsive_search_ad 96979
33.5%
expanded_dynamic_search_ad 44444
 
15.4%

Most occurring characters

ValueCountFrequency (%)
E 868578
15.9%
D 719064
13.2%
A 667940
12.2%
_ 623496
11.4%
X 340650
 
6.2%
S 335381
 
6.1%
N 333970
 
6.1%
T 296206
 
5.4%
P 289526
 
5.3%
R 238402
 
4.4%
Other values (7) 751559
13.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5464772
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 868578
15.9%
D 719064
13.2%
A 667940
12.2%
_ 623496
11.4%
X 340650
 
6.2%
S 335381
 
6.1%
N 333970
 
6.1%
T 296206
 
5.4%
P 289526
 
5.3%
R 238402
 
4.4%
Other values (7) 751559
13.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5464772
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 868578
15.9%
D 719064
13.2%
A 667940
12.2%
_ 623496
11.4%
X 340650
 
6.2%
S 335381
 
6.1%
N 333970
 
6.1%
T 296206
 
5.4%
P 289526
 
5.3%
R 238402
 
4.4%
Other values (7) 751559
13.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5464772
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 868578
15.9%
D 719064
13.2%
A 667940
12.2%
_ 623496
11.4%
X 340650
 
6.2%
S 335381
 
6.1%
N 333970
 
6.1%
T 296206
 
5.4%
P 289526
 
5.3%
R 238402
 
4.4%
Other values (7) 751559
13.8%

currency
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing400
Missing (%)0.1%
Memory size2.2 MiB
ZAR
274617 
USD
 
14909

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters868578
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowZAR
2nd rowZAR
3rd rowZAR
4th rowZAR
5th rowZAR

Common Values

ValueCountFrequency (%)
ZAR 274617
94.7%
USD 14909
 
5.1%
(Missing) 400
 
0.1%

Length

2024-04-06T15:20:30.035057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T15:20:30.223727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
zar 274617
94.9%
usd 14909
 
5.1%

Most occurring characters

ValueCountFrequency (%)
Z 274617
31.6%
A 274617
31.6%
R 274617
31.6%
U 14909
 
1.7%
S 14909
 
1.7%
D 14909
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 868578
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Z 274617
31.6%
A 274617
31.6%
R 274617
31.6%
U 14909
 
1.7%
S 14909
 
1.7%
D 14909
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 868578
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Z 274617
31.6%
A 274617
31.6%
R 274617
31.6%
U 14909
 
1.7%
S 14909
 
1.7%
D 14909
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 868578
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Z 274617
31.6%
A 274617
31.6%
R 274617
31.6%
U 14909
 
1.7%
S 14909
 
1.7%
D 14909
 
1.7%

ID
Text

Distinct185
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
2024-04-06T15:20:30.573994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length27
Mean length27
Min length27

Characters and Unicode

Total characters7828002
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowID_5da86e71bf5dee4cf5047046
2nd rowID_5da86e71bf5dee4cf5047046
3rd rowID_5da86e71bf5dee4cf5047046
4th rowID_5da86e71bf5dee4cf5047046
5th rowID_5da86e71bf5dee4cf5047046
ValueCountFrequency (%)
id_5da86e71bf5dee4cf5047046 15150
 
5.2%
id_5ee74f25f865a8154966b412 9539
 
3.3%
id_600d8eaaf5c7660c0b1f0773 8693
 
3.0%
id_5f3cdce8c0440e2c5902dd59 8658
 
3.0%
id_61321c129a9b4c145436394d 8629
 
3.0%
id_60f12ace56214677d611d526 8397
 
2.9%
id_604752a9861a02467a27c054 7790
 
2.7%
id_60e556f789173d61d07e3294 7713
 
2.7%
id_6103b50cd6d4a602e4370f05 7459
 
2.6%
id_60e5572b44311c0aa4513107 6908
 
2.4%
Other values (175) 200990
69.3%
2024-04-06T15:20:31.289321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 690365
 
8.8%
5 562134
 
7.2%
4 546438
 
7.0%
0 498968
 
6.4%
1 498002
 
6.4%
7 486120
 
6.2%
2 454026
 
5.8%
c 387724
 
5.0%
a 379980
 
4.9%
8 372094
 
4.8%
Other values (9) 2952151
37.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7828002
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 690365
 
8.8%
5 562134
 
7.2%
4 546438
 
7.0%
0 498968
 
6.4%
1 498002
 
6.4%
7 486120
 
6.2%
2 454026
 
5.8%
c 387724
 
5.0%
a 379980
 
4.9%
8 372094
 
4.8%
Other values (9) 2952151
37.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7828002
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 690365
 
8.8%
5 562134
 
7.2%
4 546438
 
7.0%
0 498968
 
6.4%
1 498002
 
6.4%
7 486120
 
6.2%
2 454026
 
5.8%
c 387724
 
5.0%
a 379980
 
4.9%
8 372094
 
4.8%
Other values (9) 2952151
37.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7828002
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 690365
 
8.8%
5 562134
 
7.2%
4 546438
 
7.0%
0 498968
 
6.4%
1 498002
 
6.4%
7 486120
 
6.2%
2 454026
 
5.8%
c 387724
 
5.0%
a 379980
 
4.9%
8 372094
 
4.8%
Other values (9) 2952151
37.7%

date
Date

Distinct1505
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
Minimum2020-01-01 00:00:00
Maximum2024-02-13 00:00:00
2024-04-06T15:20:31.724974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:32.058678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

call_type
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing253026
Missing (%)87.3%
Memory size2.2 MiB
Mobile click-to-call
36363 
Manually dialed
 
537

Length

Max length20
Median length20
Mean length19.927236
Min length15

Characters and Unicode

Total characters735315
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMobile click-to-call
2nd rowMobile click-to-call
3rd rowMobile click-to-call
4th rowMobile click-to-call
5th rowMobile click-to-call

Common Values

ValueCountFrequency (%)
Mobile click-to-call 36363
 
12.5%
Manually dialed 537
 
0.2%
(Missing) 253026
87.3%

Length

2024-04-06T15:20:32.315427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T15:20:32.505610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
mobile 36363
49.3%
click-to-call 36363
49.3%
manually 537
 
0.7%
dialed 537
 
0.7%

Most occurring characters

ValueCountFrequency (%)
l 147063
20.0%
c 109089
14.8%
i 73263
10.0%
o 72726
9.9%
- 72726
9.9%
a 37974
 
5.2%
M 36900
 
5.0%
e 36900
 
5.0%
36900
 
5.0%
b 36363
 
4.9%
Other values (6) 75411
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 735315
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 147063
20.0%
c 109089
14.8%
i 73263
10.0%
o 72726
9.9%
- 72726
9.9%
a 37974
 
5.2%
M 36900
 
5.0%
e 36900
 
5.0%
36900
 
5.0%
b 36363
 
4.9%
Other values (6) 75411
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 735315
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 147063
20.0%
c 109089
14.8%
i 73263
10.0%
o 72726
9.9%
- 72726
9.9%
a 37974
 
5.2%
M 36900
 
5.0%
e 36900
 
5.0%
36900
 
5.0%
b 36363
 
4.9%
Other values (6) 75411
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 735315
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 147063
20.0%
c 109089
14.8%
i 73263
10.0%
o 72726
9.9%
- 72726
9.9%
a 37974
 
5.2%
M 36900
 
5.0%
e 36900
 
5.0%
36900
 
5.0%
b 36363
 
4.9%
Other values (6) 75411
10.3%

call_status
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing253026
Missing (%)87.3%
Memory size2.2 MiB
Received
27257 
Missed
9643 

Length

Max length8
Median length8
Mean length7.4773442
Min length6

Characters and Unicode

Total characters275914
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMissed
2nd rowMissed
3rd rowMissed
4th rowMissed
5th rowMissed

Common Values

ValueCountFrequency (%)
Received 27257
 
9.4%
Missed 9643
 
3.3%
(Missing) 253026
87.3%

Length

2024-04-06T15:20:32.722496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T15:20:32.917318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
received 27257
73.9%
missed 9643
 
26.1%

Most occurring characters

ValueCountFrequency (%)
e 91414
33.1%
i 36900
13.4%
d 36900
13.4%
R 27257
 
9.9%
c 27257
 
9.9%
v 27257
 
9.9%
s 19286
 
7.0%
M 9643
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 275914
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 91414
33.1%
i 36900
13.4%
d 36900
13.4%
R 27257
 
9.9%
c 27257
 
9.9%
v 27257
 
9.9%
s 19286
 
7.0%
M 9643
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 275914
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 91414
33.1%
i 36900
13.4%
d 36900
13.4%
R 27257
 
9.9%
c 27257
 
9.9%
v 27257
 
9.9%
s 19286
 
7.0%
M 9643
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 275914
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 91414
33.1%
i 36900
13.4%
d 36900
13.4%
R 27257
 
9.9%
c 27257
 
9.9%
v 27257
 
9.9%
s 19286
 
7.0%
M 9643
 
3.5%

start_time
Date

MISSING 

Distinct7488
Distinct (%)20.3%
Missing253026
Missing (%)87.3%
Memory size2.2 MiB
Minimum2020-10-22 20:27:53
Maximum2024-02-13 16:55:14
2024-04-06T15:20:33.137253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:33.484431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

duration
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct473
Distinct (%)1.3%
Missing253026
Missing (%)87.3%
Infinite0
Infinite (%)0.0%
Mean67.96897
Minimum0
Maximum2125
Zeros9879
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-04-06T15:20:33.785071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median36
Q395
95-th percentile245
Maximum2125
Range2125
Interquartile range (IQR)95

Descriptive statistics

Standard deviation98.055148
Coefficient of variation (CV)1.4426458
Kurtosis37.8807
Mean67.96897
Median Absolute Deviation (MAD)36
Skewness4.0438037
Sum2508055
Variance9614.812
MonotonicityNot monotonic
2024-04-06T15:20:34.035586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9879
 
3.4%
3 594
 
0.2%
4 508
 
0.2%
2 457
 
0.2%
5 343
 
0.1%
1 304
 
0.1%
28 300
 
0.1%
19 283
 
0.1%
6 266
 
0.1%
32 264
 
0.1%
Other values (463) 23702
 
8.2%
(Missing) 253026
87.3%
ValueCountFrequency (%)
0 9879
3.4%
1 304
 
0.1%
2 457
 
0.2%
3 594
 
0.2%
4 508
 
0.2%
5 343
 
0.1%
6 266
 
0.1%
7 232
 
0.1%
8 209
 
0.1%
9 168
 
0.1%
ValueCountFrequency (%)
2125 2
 
< 0.1%
1788 3
 
< 0.1%
1390 9
< 0.1%
1378 2
 
< 0.1%
1025 2
 
< 0.1%
964 4
< 0.1%
951 3
 
< 0.1%
914 3
 
< 0.1%
905 9
< 0.1%
899 2
 
< 0.1%

end_time
Date

MISSING 

Distinct7490
Distinct (%)20.3%
Missing253026
Missing (%)87.3%
Memory size2.2 MiB
Minimum2020-10-22 20:27:53
Maximum2024-02-13 16:55:14
2024-04-06T15:20:34.287630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:34.565317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

display_location
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing253026
Missing (%)87.3%
Memory size2.2 MiB
AD
23338 
Ad
11443 
LANDING_PAGE
 
1123
Website
 
996

Length

Max length12
Median length2
Mean length2.4392954
Min length2

Characters and Unicode

Total characters90010
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAd
2nd rowAd
3rd rowAd
4th rowAd
5th rowAd

Common Values

ValueCountFrequency (%)
AD 23338
 
8.0%
Ad 11443
 
3.9%
LANDING_PAGE 1123
 
0.4%
Website 996
 
0.3%
(Missing) 253026
87.3%

Length

2024-04-06T15:20:34.810098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T15:20:35.135494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ad 34781
94.3%
landing_page 1123
 
3.0%
website 996
 
2.7%

Most occurring characters

ValueCountFrequency (%)
A 37027
41.1%
D 24461
27.2%
d 11443
 
12.7%
N 2246
 
2.5%
G 2246
 
2.5%
e 1992
 
2.2%
L 1123
 
1.2%
I 1123
 
1.2%
_ 1123
 
1.2%
P 1123
 
1.2%
Other values (6) 6103
 
6.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 90010
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 37027
41.1%
D 24461
27.2%
d 11443
 
12.7%
N 2246
 
2.5%
G 2246
 
2.5%
e 1992
 
2.2%
L 1123
 
1.2%
I 1123
 
1.2%
_ 1123
 
1.2%
P 1123
 
1.2%
Other values (6) 6103
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 90010
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 37027
41.1%
D 24461
27.2%
d 11443
 
12.7%
N 2246
 
2.5%
G 2246
 
2.5%
e 1992
 
2.2%
L 1123
 
1.2%
I 1123
 
1.2%
_ 1123
 
1.2%
P 1123
 
1.2%
Other values (6) 6103
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 90010
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 37027
41.1%
D 24461
27.2%
d 11443
 
12.7%
N 2246
 
2.5%
G 2246
 
2.5%
e 1992
 
2.2%
L 1123
 
1.2%
I 1123
 
1.2%
_ 1123
 
1.2%
P 1123
 
1.2%
Other values (6) 6103
 
6.8%

impression_share
Real number (ℝ)

Distinct45199
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.656556
Minimum0
Maximum100
Zeros282
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-04-06T15:20:35.386036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q19.99
median11.421975
Q320.122939
95-th percentile41.807143
Maximum100
Range100
Interquartile range (IQR)10.132939

Descriptive statistics

Standard deviation12.186418
Coefficient of variation (CV)0.73162892
Kurtosis7.3435747
Mean16.656556
Median Absolute Deviation (MAD)3.2413049
Skewness2.3309826
Sum4829168.7
Variance148.50879
MonotonicityNot monotonic
2024-04-06T15:20:35.655457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.99 43410
 
15.0%
5 15687
 
5.4%
9.99 3625
 
1.3%
9.99 3013
 
1.0%
100 300
 
0.1%
0 282
 
0.1%
11.04821634 135
 
< 0.1%
50 124
 
< 0.1%
5.415840824 110
 
< 0.1%
6.065072029 102
 
< 0.1%
Other values (45189) 223138
77.0%
ValueCountFrequency (%)
0 282
 
0.1%
5 15687
5.4%
5.005319149 7
 
< 0.1%
5.006038647 9
 
< 0.1%
5.006067961 8
 
< 0.1%
5.006699561 9
 
< 0.1%
5.007146199 7
 
< 0.1%
5.007886435 4
 
< 0.1%
5.007976501 8
 
< 0.1%
5.008654391 4
 
< 0.1%
ValueCountFrequency (%)
100 300
0.1%
99.49367089 3
 
< 0.1%
97.61904762 2
 
< 0.1%
97.3 2
 
< 0.1%
97.06 2
 
< 0.1%
96.98275862 1
 
< 0.1%
96.77 2
 
< 0.1%
96.65178571 2
 
< 0.1%
96.32107023 2
 
< 0.1%
96.17391304 2
 
< 0.1%

conversions_calls
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing9855
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean0.12792828
Minimum0
Maximum8
Zeros258674
Zeros (%)89.2%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-04-06T15:20:36.231488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.52636239
Coefficient of variation (CV)4.1145118
Kurtosis41.506321
Mean0.12792828
Median Absolute Deviation (MAD)0
Skewness5.6326532
Sum35829
Variance0.27705737
MonotonicityNot monotonic
2024-04-06T15:20:36.467859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 258674
89.2%
1 12334
 
4.3%
2 5697
 
2.0%
3 2197
 
0.8%
4 680
 
0.2%
5 296
 
0.1%
6 81
 
< 0.1%
7 72
 
< 0.1%
8 40
 
< 0.1%
(Missing) 9855
 
3.4%
ValueCountFrequency (%)
0 258674
89.2%
1 12334
 
4.3%
2 5697
 
2.0%
3 2197
 
0.8%
4 680
 
0.2%
5 296
 
0.1%
6 81
 
< 0.1%
7 72
 
< 0.1%
8 40
 
< 0.1%
ValueCountFrequency (%)
8 40
 
< 0.1%
7 72
 
< 0.1%
6 81
 
< 0.1%
5 296
 
0.1%
4 680
 
0.2%
3 2197
 
0.8%
2 5697
 
2.0%
1 12334
 
4.3%
0 258674
89.2%

headline1_len
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing400
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2.7455842
Minimum0
Maximum7
Zeros31252
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-04-06T15:20:36.656194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5711079
Coefficient of variation (CV)0.57223083
Kurtosis-0.50210584
Mean2.7455842
Median Absolute Deviation (MAD)1
Skewness0.044273874
Sum794918
Variance2.46838
MonotonicityNot monotonic
2024-04-06T15:20:36.854871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 71678
24.7%
2 70161
24.2%
4 48172
16.6%
5 31304
10.8%
0 31252
10.8%
1 26525
 
9.1%
6 9209
 
3.2%
7 1225
 
0.4%
(Missing) 400
 
0.1%
ValueCountFrequency (%)
0 31252
10.8%
1 26525
 
9.1%
2 70161
24.2%
3 71678
24.7%
4 48172
16.6%
5 31304
10.8%
6 9209
 
3.2%
7 1225
 
0.4%
ValueCountFrequency (%)
7 1225
 
0.4%
6 9209
 
3.2%
5 31304
10.8%
4 48172
16.6%
3 71678
24.7%
2 70161
24.2%
1 26525
 
9.1%
0 31252
10.8%

headline2_len
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing400
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2.8798243
Minimum0
Maximum7
Zeros31252
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-04-06T15:20:37.040105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile5
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.670356
Coefficient of variation (CV)0.58002013
Kurtosis-0.76204601
Mean2.8798243
Median Absolute Deviation (MAD)1
Skewness-0.12800913
Sum833784
Variance2.7900893
MonotonicityNot monotonic
2024-04-06T15:20:37.438660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 69057
23.8%
4 68373
23.6%
1 42085
14.5%
5 33758
11.6%
2 32414
11.2%
0 31252
10.8%
6 10691
 
3.7%
7 1896
 
0.7%
(Missing) 400
 
0.1%
ValueCountFrequency (%)
0 31252
10.8%
1 42085
14.5%
2 32414
11.2%
3 69057
23.8%
4 68373
23.6%
5 33758
11.6%
6 10691
 
3.7%
7 1896
 
0.7%
ValueCountFrequency (%)
7 1896
 
0.7%
6 10691
 
3.7%
5 33758
11.6%
4 68373
23.6%
3 69057
23.8%
2 32414
11.2%
1 42085
14.5%
0 31252
10.8%

ad_description_len
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)< 0.1%
Missing400
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean10.706165
Minimum0
Maximum20
Zeros31252
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-04-06T15:20:37.675229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median12
Q314
95-th percentile16
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.5094501
Coefficient of variation (CV)0.42120127
Kurtosis0.8903601
Mean10.706165
Median Absolute Deviation (MAD)2
Skewness-1.289286
Sum3099713
Variance20.33514
MonotonicityNot monotonic
2024-04-06T15:20:37.906890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
13 51850
17.9%
14 38077
13.1%
11 37260
12.9%
12 33490
11.6%
0 31252
10.8%
10 26916
9.3%
15 15303
 
5.3%
16 12563
 
4.3%
9 10799
 
3.7%
7 9397
 
3.2%
Other values (11) 22619
7.8%
ValueCountFrequency (%)
0 31252
10.8%
1 1
 
< 0.1%
2 723
 
0.2%
3 1342
 
0.5%
4 1571
 
0.5%
5 3893
 
1.3%
6 2504
 
0.9%
7 9397
 
3.2%
8 4843
 
1.7%
9 10799
 
3.7%
ValueCountFrequency (%)
20 105
 
< 0.1%
19 303
 
0.1%
18 637
 
0.2%
17 6697
 
2.3%
16 12563
 
4.3%
15 15303
 
5.3%
14 38077
13.1%
13 51850
17.9%
12 33490
11.6%
11 37260
12.9%

Interactions

2024-04-06T15:20:20.628834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:19:58.712070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:01.517942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:03.857090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:06.758728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:08.984179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:10.925473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:13.566894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:16.008271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:18.285087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:20.881002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:19:58.984262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:01.755975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:04.132066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:06.993767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:09.148819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:11.214881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:13.816952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:16.246682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:18.545549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:21.105179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:19:59.210737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:01.973936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:04.464177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:07.212867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:09.326424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:11.474323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:14.103115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:16.462875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:18.770968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:21.362103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:19:59.483076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:02.317221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:04.795751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:07.461119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:09.512702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:11.829116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:14.359751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:16.704635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:19.017309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:21.550497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:19:59.781741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:02.487492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:05.073789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:07.635433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:09.683163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:12.035215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:14.549474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:16.886944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:19.208148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:21.768211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:00.060476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:02.699085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:05.373043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:07.856371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:09.842245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:12.249505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:14.788730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:17.106691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:19.431131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:22.150964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:00.307430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:02.911476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:05.632517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:08.076435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:10.004121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:12.626184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:15.015372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:17.328300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:19.653680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:22.405840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:00.678523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:03.149276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:05.893051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:08.321384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:10.190225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:12.867058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:15.266665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:17.565815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:19.906002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:22.867196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:00.967741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:03.375676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:06.256727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:08.555617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:10.369302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:13.092917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:15.504340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:17.802008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:20.150028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:23.209247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:01.254703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:03.618562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:06.503338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:08.787748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:10.627135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:13.332240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:15.743527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:18.040912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T15:20:20.384651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T15:20:38.107465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ad_description_lenad_typecall_statuscall_typeclicksconversionsconversions_callscostcurrencydisplay_locationdurationheadline1_lenheadline2_lenimpression_shareimpressions
ad_description_len1.0000.5750.0640.0650.1240.0650.0400.0980.0630.2370.0020.4090.381-0.0590.176
ad_type0.5751.0000.0590.0480.155-0.005-0.0380.2150.2220.3030.0470.6690.467-0.0560.256
call_status0.0640.0591.0000.004-0.0180.0640.3270.0030.0000.0880.7620.009-0.0130.030-0.031
call_type0.0650.0480.0041.000-0.061-0.1740.009-0.0610.0040.4600.052-0.057-0.0310.108-0.065
clicks0.1240.155-0.018-0.0611.0000.1870.0910.8980.0081.000-0.0210.0260.313-0.0190.847
conversions0.065-0.0050.064-0.1740.1871.0000.3840.1820.0080.1940.0730.0390.079-0.0370.156
conversions_calls0.040-0.0380.3270.0090.0910.3841.0000.0810.0680.1470.5350.014-0.0170.0860.052
cost0.0980.2150.003-0.0610.8980.1820.0811.0000.0070.0210.0030.0700.3160.0340.783
currency0.0630.2220.0000.0040.0080.0080.0680.0071.0000.100-0.012-0.0810.0010.033-0.010
display_location0.2370.3030.0880.4601.0000.1940.1470.0210.1001.000-0.171-0.0240.036-0.2750.010
duration0.0020.0470.7620.052-0.0210.0730.5350.003-0.012-0.1711.000-0.022-0.0100.136-0.034
headline1_len0.4090.6690.009-0.0570.0260.0390.0140.070-0.081-0.024-0.0221.0000.332-0.0980.104
headline2_len0.3810.467-0.013-0.0310.3130.079-0.0170.3160.0010.036-0.0100.3321.000-0.0030.392
impression_share-0.059-0.0560.0300.108-0.019-0.0370.0860.0340.033-0.2750.136-0.098-0.0031.000-0.076
impressions0.1760.256-0.031-0.0650.8470.1560.0520.783-0.0100.010-0.0340.1040.392-0.0761.000

Missing values

2024-04-06T15:20:23.637547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T15:20:24.465370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-04-06T15:20:26.041934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

impressionsclickscostconversionsad_typecurrencyIDdatecall_typecall_statusstart_timedurationend_timedisplay_locationimpression_shareconversions_callsheadline1_lenheadline2_lenad_description_len
0142.015.03393.00.0EXPANDED_TEXT_ADZARID_5da86e71bf5dee4cf50470462020-01-01NaNNaNNaNNaNNaNNaN16.2796690.02.05.011.0
189.08.01817.00.0EXPANDED_TEXT_ADZARID_5da86e71bf5dee4cf50470462020-01-01NaNNaNNaNNaNNaNNaN16.2796690.02.02.013.0
259.08.01743.00.0EXPANDED_TEXT_ADZARID_5da86e71bf5dee4cf50470462020-01-01NaNNaNNaNNaNNaNNaN16.2796690.02.02.010.0
378.04.0917.00.0EXPANDED_TEXT_ADZARID_5da86e71bf5dee4cf50470462020-01-01NaNNaNNaNNaNNaNNaN16.2796690.02.03.013.0
420.01.0217.00.0EXPANDED_TEXT_ADZARID_5da86e71bf5dee4cf50470462020-01-01NaNNaNNaNNaNNaNNaN16.2796690.02.02.013.0
55.00.00.00.0EXPANDED_TEXT_ADZARID_5da86e71bf5dee4cf50470462020-01-01NaNNaNNaNNaNNaNNaN16.2796690.02.04.010.0
6149.021.04091.00.0EXPANDED_TEXT_ADZARID_5da86e71bf5dee4cf50470462020-01-02NaNNaNNaNNaNNaNNaN16.2270890.02.05.011.0
778.011.02199.00.0EXPANDED_TEXT_ADZARID_5da86e71bf5dee4cf50470462020-01-02NaNNaNNaNNaNNaNNaN16.2270890.02.02.013.0
881.010.02024.00.0EXPANDED_TEXT_ADZARID_5da86e71bf5dee4cf50470462020-01-02NaNNaNNaNNaNNaNNaN16.2270890.02.03.013.0
952.04.0706.00.0EXPANDED_TEXT_ADZARID_5da86e71bf5dee4cf50470462020-01-02NaNNaNNaNNaNNaNNaN16.2270890.02.02.010.0
impressionsclickscostconversionsad_typecurrencyIDdatecall_typecall_statusstart_timedurationend_timedisplay_locationimpression_shareconversions_callsheadline1_lenheadline2_lenad_description_len
289916NaNNaNNaNNaNNaNNaNID_6567476a4a967518c623d5062024-02-02NaNNaNNaNNaNNaNNaN10.0725230.0NaNNaNNaN
289917NaNNaNNaNNaNNaNNaNID_6567476a4a967518c623d5062024-02-03NaNNaNNaNNaNNaNNaN10.6143760.0NaNNaNNaN
289918NaNNaNNaNNaNNaNNaNID_656747739b5c72023947f7c72024-02-02NaNNaNNaNNaNNaNNaN9.9900000.0NaNNaNNaN
289919NaNNaNNaNNaNNaNNaNID_656747739b5c72023947f7c72024-02-03NaNNaNNaNNaNNaNNaN9.9900000.0NaNNaNNaN
289920NaNNaNNaNNaNNaNNaNID_656747739b5c72023947f7c72024-02-04NaNNaNNaNNaNNaNNaN9.9900000.0NaNNaNNaN
289921NaNNaNNaNNaNNaNNaNID_65687e2b40ea026fcc662a752024-02-03NaNNaNNaNNaNNaNNaN12.0356820.0NaNNaNNaN
289922NaNNaNNaNNaNNaNNaNID_65687e2b40ea026fcc662a752024-02-04NaNNaNNaNNaNNaNNaN15.7110230.0NaNNaNNaN
289923NaNNaNNaNNaNNaNNaNID_65a7bf329fa4627faf28390c2024-02-03NaNNaNNaNNaNNaNNaN15.4644250.0NaNNaNNaN
289924NaNNaNNaNNaNNaNNaNID_65b0f65c7fe62e56c5593d552024-02-03NaNNaNNaNNaNNaNNaN10.0164810.0NaNNaNNaN
289925NaNNaNNaNNaNNaNNaNID_65b0f65c7fe62e56c5593d552024-02-04NaNNaNNaNNaNNaNNaN9.9900000.0NaNNaNNaN

Duplicate rows

Most frequently occurring

impressionsclickscostconversionsad_typecurrencyIDdatecall_typecall_statusstart_timedurationend_timedisplay_locationimpression_shareconversions_callsheadline1_lenheadline2_lenad_description_len# duplicates
01.00.00.00.0EXPANDED_DYNAMIC_SEARCH_ADZARID_5da86e71bf5dee4cf50470462020-04-15NaNNaNNaNNaNNaNNaN24.8996340.01.01.010.02
11.00.00.00.0EXPANDED_DYNAMIC_SEARCH_ADZARID_5da86e71bf5dee4cf50470462020-05-01NaNNaNNaNNaNNaNNaN32.0770310.01.01.010.02
21.00.00.00.0EXPANDED_DYNAMIC_SEARCH_ADZARID_5da86e71bf5dee4cf50470462020-06-19NaNNaNNaNNaNNaNNaN31.8403340.01.01.010.02
31.00.00.00.0EXPANDED_DYNAMIC_SEARCH_ADZARID_5da86e71bf5dee4cf50470462020-06-23NaNNaNNaNNaNNaNNaN28.2086700.01.01.010.02
41.00.00.00.0EXPANDED_DYNAMIC_SEARCH_ADZARID_5da86e71bf5dee4cf50470462020-06-27NaNNaNNaNNaNNaNNaN19.4142530.01.01.010.02
51.00.00.00.0EXPANDED_DYNAMIC_SEARCH_ADZARID_5da86e71bf5dee4cf50470462020-07-04NaNNaNNaNNaNNaNNaN21.2646450.01.01.010.02
61.00.00.00.0EXPANDED_DYNAMIC_SEARCH_ADZARID_5da86e71bf5dee4cf50470462020-07-07NaNNaNNaNNaNNaNNaN21.8859521.01.01.010.02
71.00.00.00.0EXPANDED_DYNAMIC_SEARCH_ADZARID_5da86e71bf5dee4cf50470462020-07-07NaNNaNNaNNaNNaNNaN21.8859521.01.01.013.02
81.00.00.00.0EXPANDED_DYNAMIC_SEARCH_ADZARID_5da86e71bf5dee4cf50470462020-07-31NaNNaNNaNNaNNaNNaN26.0685390.01.01.010.02
91.00.00.00.0EXPANDED_DYNAMIC_SEARCH_ADZARID_5da86e71bf5dee4cf50470462020-08-14NaNNaNNaNNaNNaNNaN22.0842450.01.01.010.02